Volumetric imaging technique for medical imaging processing system

ABSTRACT

A medical imaging communication system processes medical images for being transmitted to a client device. The system receives a set of images, each image corresponding to a slice of a three-dimensional object. The system combines a first subset of the images into a first combined image and combines a second subset of the images into a second combined image. The first and second combined image are compressed using a lossless compression algorithm and transmitted to the client device.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.62/941,727, filed Nov. 28, 2019, which is incorporated by reference inits entirety.

FIELD OF THE DISCLOSURE

The present disclosure relates to a medical imaging processing systemand more specifically to a secure and efficient system for transmittingand manipulating digital medical images.

BACKGROUND

Being able to collaborate in the medical field is beneficial in manyways. By collaborating, medical professionals are able to get opinionsabout a patient's diagnosis from third parties (such as specialist) toincrease the accuracy and quality of the patient's care. Moreover,collaboration enables entities to obtain a larger amount of data forconducting large scale medical studies to further the understanding ofvarious diseases and/or techniques used to treat the diseases. However,while collaboration has many benefits, it is also important to protectpatient privacy while sharing the medical information of the patient.

Moreover, with the advancement of technology, client devices areubiquitous. For example, oftentimes, people have close access to mobiledevices for accessing content. However, the use of mobile devices comeswith some drawbacks. For instance, mobile devices are limited in powerand storage capabilities. Additionally, mobile devices may be limited inbandwidth for transferring data in or out of the mobile device.Furthermore, as the medical technology increases, the amount of data andcomplexity of the data generated by some medical tests also increases.As such, it would be beneficial to develop an efficient way for allowinga mobile device to consume this medical data as well as present it giventhe technical constraints of such devices (e.g., relatively smallviewing areas, limited processing capabilities and power consumptionconsiderations).

SUMMARY

Embodiments relate to a medical imaging communication system. By way ofexample, the medical imaging communication system receives medical testpackages, processes the medical test packages, stores the medical testpackages, and controls the distribution of the medical test packages.

In some embodiments, the medical imaging communication systemdeidentifies medical test packages for protecting the privacy ofpatients. The system receives a medical test package. The medical testpackage includes at least patient identifiable information and one ormore medical digital images. The system identifies the patientidentifiable information from the received medical test package. Thesystem generates encrypted patient identifiable information byencrypting the patient identifiable information using an encryption key.The system deidentifies the medical test package by replacing thepatient identifiable information in the medical test package with theencrypted patient identifiable information. The deidentified medicaltest package is transmitted to one or more third-party users.

In other embodiments, the medical imaging communication system processesmedical images for being transmitted to a client device. The systemreceives a set of images, each image corresponding to a slice of athree-dimensional object. The system combines a first subset of theimages into a first combined image and combines a second subset of theimages into a second combined image. The first and second combined imageare compressed using a lossless compression algorithm and transmitted tothe client device.

In yet other embodiments, the medical imaging communication systemgenerates a graphical user interface for displaying medical testinformation. The system receives, from a reviewing user, one or morecomments for an image, the one or more comments associated with aportion of the image, each comment corresponding to an observation bythe reviewing user on the corresponding portion of the image. The systemidentifies, from each comment, the portion of the image associated withthe comment. The system generates an annotated image by overlaying thecomment and an indication of the identified portion of the imageassociated with the comment.

In yet other embodiments, the medical imaging communication systemgenerates an automated report for a medical image corresponding to apatient. The system receives one or more medical images for a patient.The system applies a model to identify an abnormality in the receivedone or more medical images. The model is configured to determine alikelihood score for the presence of the abnormality in the one or moremedical images. If the likelihood score is above a threshold value, thesystem generates an automated report indicating the type of abnormalityidentified in the received one or more medical images. The automatedreport is then provided to a viewing user (e.g., a medical professionalviewing the one or more medical images).

BRIEF DESCRIPTION OF THE DRAWINGS

The teachings of the embodiments can be readily understood byconsidering the following detailed description in conjunction with theaccompanying drawings.

FIG. (FIG. 1 is a block diagram illustrating components of an examplemachine able to read instructions from a machine-readable medium andexecute them in a processor (or controller).

FIG. 2 illustrates a system architecture of a medical imagingcommunication system, according to one embodiment.

FIG. 3 illustrates a system environment for the deidentification ofmedical test packages, according to one embodiment.

FIG. 4 illustrates a block diagram of the deidentification module of themedical imaging communication system, according to one embodiment.

FIG. 5 illustrates a flow diagram of a process for deidentifying medicaltest packages, according to one embodiment.

FIGS. 6A and 6B illustrate system environments for the compression of aset of slice images, according to various embodiments.

FIG. 6C illustrates an example set of slice images, according to oneembodiment.

FIG. 7A illustrates a system environment for the decompression of a setof slice images, according to one embodiment.

FIG. 7B illustrates a system environment for the generating athree-dimensional (3D) model from a set of slides, according to oneembodiment.

FIG. 8 illustrates a block diagram of the volumetric imaging module ofthe medical imaging communication system in communication with avolumetric imaging client of a client device, according to oneembodiment.

FIG. 9 illustrates a flow diagram of a process for rendering a3-dimensional image from a set of slice images, according to oneembodiment.

FIG. 10A illustrates a system environment for processing digital medicalimages in a collaborative way, according to one embodiment.

FIG. 10B illustrates a user interface for viewing a medical reportcorresponding to a medical image, according to one embodiment.

FIG. 11 illustrates a block diagram of the visual reporting module ofthe medical imaging communication system, according to one embodiment.

FIG. 12 illustrates a flow diagram of a process for providingcollaborative reports of a digital medical image, according to oneembodiment.

FIG. 13 illustrates a flow diagram of a process for providing anautomated analysis of medical images, according to one embodiment.

DETAILED DESCRIPTION

The Figures (FIG.) and the following description relate to preferredembodiments by way of illustration only. It should be noted that fromthe following discussion, alternative embodiments of the structures andmethods disclosed herein will be readily recognized as viablealternatives that may be employed without departing from the principlesof the embodiments.

Reference will now be made in detail to several embodiments, examples ofwhich are illustrated in the accompanying figures. It is noted thatwherever practicable, similar or like reference numbers may be used inthe figures and may indicate similar or like functionality. The figuresdepict embodiments for purposes of illustration only.

Configuration Overview

One embodiment of a disclosed system, method and computer readablestorage medium for a medical imaging communication system. The medicalimaging communication system receives medical test packages, processesthe medical test packages, stores the medical test packages, andcontrols the distribution of the medical test packages.

In some embodiments, the medical imaging communication systemdeidentifies medical test packages for protecting the privacy ofpatients. The system receives a medical test package. The medical testpackage includes at least patient identifiable information and one ormore medical digital images. The system identifies the patientidentifiable information from the received medical test package. Thesystem generates an encrypted patient identifiable information byencrypting the identified patient identifiable information using anencryption key. The system deidentifies the medical test package byreplacing the patient identifiable information in the medical testpackage with the encrypted patient identifiable information. Thedeidentified medical test package is sent to one or more third-partyusers. Moreover, in some embodiments, the deidentified medical testpackage is stored in an external storage medium (e.g., using a cloudstorage service)

Moreover, the deidentification of the medical test package may includehashing, using a one-way cryptographic hash algorithm, one of a patientidentification (patient ID) or a study identification (study ID) fromthe received medical test package to generate a hashed identification.The hashed identification is then inserted to the deidentified medicaltest package to enable users to match multiple deidentified medical testpackages to each other.

In one embodiment, the encrypted patient identifiable information isunable to be decrypted without a decryption key. Additionally, in oneembodiment, the patient identifiable information includes the patient'spersonal identifiable information (PII) such as a patient's name.

In one embodiment, the one or more third-party users include acollaborator for providing a report including an analysis for the one ormore medical digital images included in the deidentified medical testpackage.

In one embodiment, the one or more third-party users include an entityconducting a medical study.

In one embodiment, deidentified medical test package is sent to the oneor more third-party users using the Digital Imaging and Communication inMedicine (DICOM) standard.

Moreover, in some embodiments, the deidentified medical test package isreceived at a client device. The client device then retrieves adecryption key for decrypting the encrypted patient identifiableinformation. The client device identifies the encrypted patientidentifiable information from the deidentified medical test package anddecrypts the identified encrypted patient identifiable information. Ifthe decryption is successful, the client device displays the decryptedpatient identifiable information and the one or more medical digitalimages from the deidentified medical test package. In one embodiment,the decryption key is stored in a secure location of the client device.For example, the decryption key is installed in the secure location ofthe client device by a system administrator of a health care institutionassociated with the decryption key.

Alternatively, in one embodiment, the deidentified medical test packageis received at a client device. The client device then determines if adecryption key for decrypting the encrypted patient identifiableinformation is available. If the decryption key is not available, theclient device displays the one or more medical digital images as beingattributed to an anonymous patient. Otherwise, if the decryption key isavailable, the client device decrypts the identified encrypted patientidentifiable information, and displays the decrypted patientidentifiable information and the one or more medical digital images fromthe deidentified medical test package.

In one embodiment, to determine whether decryption key for decryptingthe encrypted patient identifiable information is available, the clientdevice retrieves a potential decryption key and attempts to decrypt theencrypted patient identifiable information using the potentialdecryption key. If the decryption attempt fails, the client devicedetermines that the decryption key is unavailable.

For example, in attempting to decrypt the encrypted patient identifiableinformation using the potential decryption key, the client deviceperforms a decryption operation on the encrypted patient identifiableinformation using the potential decryption key to generate a decryptedvalue. If the resulting decrypted value does not match a predeterminedformat, the client device determines that the decryption failed. Forinstance, the client device may compare a digest of the decrypted valueto an expected digest for the encrypted patient identifiableinformation. If the two digests do not match, the client devicedetermines that the potential decryption key is not the decrepitationkey for decrypting the encrypted patient identifiable information.

In other embodiments, the medical imaging communication system processesmedical images for being transmitted to a client device. The systemreceives a set of images, each image corresponding to a slice of athree-dimensional object. The system combines a first subset of theimages into a first combined image and combines a second subset of theimages into a second combined image. The first and second combined imageare compressed using a lossless compression algorithm and sent to theclient device.

In one embodiment, the first combined image is generated by arrangingeach pixel of each image of the first subset of images into the firstcombined image. In one embodiment, the first combined image is generatedby interlacing the images in the first subset of images.

In one embodiment, the images in the set of images have a first bitdepth. Moreover, the images in first subset of images are combined intoa third combined image. The first combined image and the third combinedimage have a second bit depth, the second bit depth lower than the firstbit depth.

In one embodiment, for each pixel of each image in the first subset ofimages, a location associated with the pixel in the first combined imageis determined, a new pixel having the second bit depth from the pixelhaving the first bit depth is determined, and the new pixel is stored inthe first combined image at the determined location associated with thepixel.

In one embodiment, the set of images are medical images captured using amedical imaging device. In one embodiment, the medical imaging device isone of a computer tomography (CT) scanner, a magnetic resonance imaging(MRI) scanner, an X-ray machine, and an ultrasound machine.

In one embodiment, the set of images are received using the DigitalImaging and Communication in Medicine (DICOM) standard.

In one embodiment, a slice is a two-dimensional image of thethree-dimensional object across a specific plane at a specific depth

In yet other embodiments, the medical imaging communication systemgenerates a graphical user interface (GUI) that is provided (or enabled)to display medical test information on a screen of a computing device.The system receives, from a reviewing user, one or more comments for animage, the one or more comments associated with a portion of the image,each comment corresponding to an observation by the reviewing user onthe corresponding portion of the image. The system identifies, from eachcomment, the portion of the image associated with the comment. Thesystem generates an annotated image by overlaying the comment and anindication of the identified portion of the image associated with thecomment. In one embodiment, the system further generates a web page. Theweb page may include at least the annotated image and the received oneor more comments for an image

In one embodiment, to identify the portion of the image associated withthe comment, a description of a location within the image is identifiedfrom the one or more comments.

In one embodiment, the description of the location within the image isidentified by applying a trained natural language processing (NLP) modelto the one or more comments for the image.

In one embodiment, the NLP model is trained using a machine learningalgorithm and a training set. The training set includes a plurality oflabeled comments, each labeled comment of the plurality of labeledcomments indicating a portion of the labeled comment corresponding to adescription of a location.

In one embodiment, to identify the portion of the image associated withthe comment, a trained image recognition model is applied to identifyone or more objects included in the identified description of thelocation within the image.

In one embodiment, the image recognition model is trained using amachine learning algorithm and a training set. The training set includesa plurality of labeled images, each labeled image of the plurality oflabeled images including a description of location within the image andan indication of the location corresponding to the description.

In yet other embodiments, the medical imaging communication systemgenerates an automated report for a medical image corresponding to apatient. The system receives one or more medical images for a patient.The system applies a model to identify an abnormality in the receivedone or more medical images. The model is configured to determine alikelihood score for the presence of the abnormality in the one or moremedical images. If the likelihood score is above a threshold value, thesystem generates an automated report indicating the type of abnormalityidentified in the received one or more medical images. The automatedreport is then provided to a viewing user (e.g., a medical professionalviewing the one or more medical images).

In one embodiment, to apply a model to determine the likelihood score,an image recognition model is first applied to the one or more medicalimages to determine a type of object depicted in the one or more medicalimages. Based on the type of object depicted, one or more abnormalitydetection models corresponding to the determined type of object areidentified. Each abnormality detection model is trained to detect one ormore types of abnormalities for the determined type of object depictedin the one or more medical images.

In one embodiment, prior to applying a model to determine the likelihoodscore, one or more comments for the medical image are received from areviewing user. Each comment corresponds to an observation by thereviewing user of the image. Natural language processing is then appliedto the one or more comments to identify a potential abnormality observedby the reviewing user. Based on the outcome of the natural languageprocessing model, one or more abnormality detection models correspondingto the identified potential abnormality are identified.

In one embodiment, a modification for the generated automated report isreceived from the viewing user. Based on the received modification, themodels applied to the one or more medical images are retrained.

Computing Machine Architecture

FIG. (FIG. 1 is a block diagram illustrating components of an examplemachine able to read instructions from a machine-readable medium andexecute them in a processor (or controller). Specifically, FIG. 1 showsa diagrammatic representation of a machine in the example form of acomputer system 100 within which instructions 124 (e.g., software orprogram code) that when executed caused the machine to perform any oneor more of the methodologies discussed herein. In alternativeembodiments, the machine operates as a standalone device or may beconnected (e.g., networked) to other machines. In a networkeddeployment, the machine may operate in the capacity of a server machineor a client machine in a server-client network environment, or as a peermachine in a peer-to-peer (or distributed) network environment.

The machine may be a server computer, a client computer, a personalcomputer (PC), a tablet PC, a set-top box (STB), a personal digitalassistant (PDA), a cellular telephone, a smartphone, a web appliance, anetwork router, switch or bridge, or any machine capable of executinginstructions 124 (sequential or otherwise) that specify actions to betaken by that machine. Further, while only a single machine isillustrated, the term “machine” shall also be taken to include anycollection of machines that individually or jointly execute instructions124 to perform any one or more of the methodologies discussed herein.

The example computer system 100 includes a processor 102 (e.g., acentral processing unit (CPU), a graphics processing unit (GPU), adigital signal processor (DSP), one or more application specificintegrated circuits (ASICs), one or more radio-frequency integratedcircuits (RFICs), or any combination of these), a main memory 104, and astatic memory 106, which are configured to communicate with each othervia a bus 108. The computer system 100 may further include graphicsdisplay unit 110 (e.g., a plasma display panel (PDP), a liquid crystaldisplay (LCD), a projector, or a cathode ray tube (CRT)). The computersystem 100 may also include alphanumeric input device 112 (e.g., akeyboard), a cursor control device 114 (e.g., a mouse, a trackball, ajoystick, a motion sensor, or other pointing instrument), a storage unit116, a signal generation device 118 (e.g., a speaker), and a networkinterface device 820, which also are configured to communicate via thebus 108.

The storage unit 116 includes a machine-readable medium 122 on which isstored instructions 124 (e.g., software) embodying any one or more ofthe methodologies or functions described herein. The instructions 124may also reside, completely or at least partially, within the mainmemory 104 or within the processor 102 (e.g., within a processor's cachememory) during execution thereof by the computer system 100, the mainmemory 104 and the processor 102 also constituting machine-readablemedia. The instructions 124 (e.g., software) may be transmitted orreceived over a network 126 via the network interface device 120.

While machine-readable medium 122 is shown in an example embodiment tobe a single medium, the term “machine-readable medium” should be takento include a single medium or multiple media (e.g., a centralized ordistributed database, or associated caches and servers) able to storeinstructions (e.g., instructions 124). The term “machine-readablemedium” shall also be taken to include any medium that is capable ofstoring instructions (e.g., instructions 124) for execution by themachine and that cause the machine to perform any one or more of themethodologies disclosed herein. The term “machine-readable medium”includes, but not be limited to, data repositories in the form ofsolid-state memories, optical media, and magnetic media.

System Architecture

FIG. 2 illustrates a system architecture 200 of a medical imagingcommunication system 260, according to one embodiment. The medicalimaging communication system 260 may be accessed by multiple medicalprofessionals (e.g., doctors or physicians, surgeons, nurses,technicians, and administrators), as well as patients and third-partyentities (e.g., scientists or third-party doctors conducting large scalescientific or medical studies).

The system architecture 200 includes the medical imaging communicationsystem 260, a medical imaging device 250, and multiple client devices210. The client devices are used by various entities accessing themedical imaging communication system 260 to access or manipulate theinformation stored in the medical imaging communication system 260. Forexample, a medical provider 220 uses a first client device 210A toaccess medical images corresponding to a patient 230 to provide adiagnosis for the patient 230. A collaborator 225, such as a specialistor third-party doctor, uses a second client device 210B to access themedical images corresponding to the patient 230 to provide comments onthe images for the doctor's consideration, or to use the images in alarge-scale medical study. The information accessed by the collaboratormay be deidentified to protect the privacy of the patient 230. Forexample, the personal identifiable information (PII) of the patient isremoved from the information available to the collaborator in a way thatthe collaborator can provide meaningful comments on the medical imageswithout being able to determine which patient the medical images relateto. Moreover, the patient 230 may also be able to access the medicalimaging communication system 260. For instance, the patient 230 is ableto access his or her own medical images for his or her own records.

The client devices 210 are one or more computing devices capable ofreceiving user input as well as transmitting and/or receiving data viathe network 220. In one embodiment, a client device 210 is aconventional computer system, such as a desktop or a laptop computer.Alternatively, a client device 210 may be a device having computerfunctionality, such as a personal digital assistant (PDA), a mobiletelephone, a smartphone, or another suitable device. A client device 210is configured to communicate via the network 220. In one embodiment, aclient device 210 executes an application allowing a user of the clientdevice 210 to interact with the medical imaging communication system260. For example, a client device 210 executes a browser application toenable interaction between the client device 210 and the medical imagingcommunication system 260 via the network 220. In another embodiment, aclient device 210 interacts with the medical imaging communicationsystem 260 through an application programming interface (API) running ona native operating system of the client device 210, such as IOS® orANDROID™.

The client devices 210 are configured to communicate via the network220, which may comprise any combination of local area and/or wide areanetworks, using both wired and/or wireless communication systems. In oneembodiment, the network 220 uses standard communications technologiesand/or protocols. For example, the network 220 includes communicationlinks using technologies such as Ethernet, 802.11, worldwideinteroperability for microwave access (WiMAX), 3G, 4G, code divisionmultiple access (CDMA), digital subscriber line (DSL), etc. Examples ofnetworking protocols used for communicating via the network 220 includemultiprotocol label switching (MPLS), transmission controlprotocol/Internet protocol (TCP/IP), hypertext transport protocol(HTTP), simple mail transfer protocol (SMTP), and file transfer protocol(FTP). Data exchanged over the network 220 may be represented using anysuitable format, such as hypertext markup language (HTML) or extensiblemarkup language (XML). In some embodiments, all or some of thecommunication links of the network 220 may be encrypted using anysuitable technique or techniques.

The medical imaging device 250 captures medical images and provides themedical images to the medical imaging communication system 260 forstorage. For example, the medical imaging device 250 is a computerizedtomography (CT) imaging system that produces 2-dimensional images of a“slice” or cross-section of the body of the patient 230. In anotherexample, the medical imaging device 250 is a magnetic resonance imaging(MM) system, an ultrasound (US) system, an X-Ray system, a digitalradiography (DX) system, a computerized radiography (CR) system, anelectrocardiography (ECG) system, or any other suitable medical imagingdevice.

In some embodiments, the medical imaging device 250 is operated by anoperator 240. For example, the operator 240 may be a technician or aradiologist. The operator 240 operates the medical imaging device 250according to instructions provided by the medical provider 220 of thepatient 230. The operator 240 further accesses the medical imagingcommunication system 260 (e.g., through the medical imaging device) tostore the medical images captured by the medical imaging device 250.

The medical imaging device 250 communicates with the medical imagingcommunication system 260 using a specified communication standard. Forexample, the medical imaging device 250 uses the Digital Imaging andCommunication in Medicine (DICOM) standard. The medical imaging device250 formats the patient medical images and the patient data in a DICOMfile as specified by the DICOM standard, and transmits the DICOM file tothe medical imaging communication system 260.

Medical Imaging and Communication System

The medical imaging communication system 260 receives patient medicalimages and patient data from various sources and stores the receiveddata securely. In some embodiments, the medical imaging communicationsystem 260 further controls access to the patient information andprevents unauthorized entities from accessing sensitive patientinformation. The medical imaging communication system 260 may include,among other components, a deidentification module 270, and a contentstore 275, a volumetric imaging module 280, a visual reporting module285. The functionality of each module may be embodied as instructionsand may be stored in a storage, e.g., main memory 104 and/or storageunit 116, and executable by a processor, e.g., processor 102. Thefunctionality of each module also may be embodied in applicationspecific integrated circuits (ASICs) and/or field programmable gatearrays (FPGAs).

Although in the example of FIG. 2 , the medical imaging communicationsystem 260 is shown as a single system, the medical imagingcommunication system 260 may be a decentralized system, or may becomposed of multiple independent systems. For example, the content store275 may be a decentralized cloud storing system. Additionally, one ormore components shown in FIG. 2 as being part of the medical imagingcommunication system 260 may be instead implemented in each of theclient devices connected via the network 220. For example, instead ofhaving a centralized deidentification module 270, each client device 210(or a subset thereof) and/or the medical imaging device 250 may locallyimplement an instance of the deidentification module 270 such that thefunctions of the deidentification module 270 are performed in adecentralized fashion in each client device 210.

A. Patient Deidentification

The deidentification module 270 receives a medical test package andremoves personal identifiable information (PII) prior to storing themedical test package in the store 275. For example, the medical testpackage may be a file containing medical images and formatted using aformat compliant with the DICOM standard. FIG. 3 illustrates a systemenvironment for the deidentification of medical test packages 310,according to one embodiment.

The deidentification module 270 deidentifies (e.g., anonymizes) medicaltest packages 310 received from a medical imaging device 250. In someembodiments, the deidentification module 270 is further configured todeidentify other types of patient information to remove PII related tothe patient before being stored in content store 275 or beingdistributed to client devices 210B of collaborators 225.

A patient's medical test package 310 includes patient information (e.g.,patient's name, patient's address, patient's identification number,patient's age, patient's gender, etc.), test information (e.g., testdate, test duration, test technique, test parameters, patient'ssymptoms, etc.), one or more test results (e.g., one or more medicalimages (such as CT scans, CAT scans, MRI scans, X-Rays, ultrasounds,etc.), one or more analytical test results, etc.), and one or morecomments or findings. In some embodiments, the patient's medical testpackage 310 includes additional information (e.g., operator/technicianinformation, test location information, etc.).

The medical test package 310 may be provided to the medical imagingcommunication system 260 for storage from the medical imaging device250. The medical test package 310 is formatted with a predeterminedformat. The medical test package 310 may be transmitted to the medicalimaging communication system 260 in an encrypted form to protect thepatient's privacy.

To deidentify the medical test package 310, the deidentification module270 uses an encryption key 320 to modify the patient PII before storingthe deidentified medical test package 315. As such, without a decryptionkey 325 that corresponds to the encryption key 320, an entity is unableto obtain the patient PII from the stored deidentified medical testpackage 315. In some embodiments, the deidentification module 270identifies which portion of the medical test package 310 containspatient's PII and encrypts the identified portions. For example, thedeidentification module 270 identifies portions of the patient'sinformation (such as patient's name, patient's address, and patient'sidentification number) and replaces the identified patient's PII with anencrypted version of the patient's PII. As a result, the deidentifiedmedical test package 315 includes the encrypted version of the patient'sPII and unencrypted version (or plaintext version) of the non-PIIinformation.

In some embodiments, the deidentification module 270 uses symmetricencryption, where the encryption key is kept secret and may be used forboth encrypting and decrypting the patient PII to deidentify a patient'smedical test package. As such, an entity that has the encryption key isable to decrypt the patient PII to determine which patient the medicaltest package 310 corresponds to.

In other embodiments, the deidentification module 270 uses asymmetricencryption, where an encryption key might be made public to allow anyentity to use the encryption key to encrypt the patient PII. Forinstance, if a first entity wants to share a patient's medical testpackage 310 with a second entity in a way that the second entity is ableto determine the identity of the patient, the first entity candeidentify the patient's medical test package 310 using the secondentity's encryption key. That way, if the second entity's decryption keyis kept secret, entities other than the second entity that do not haveaccess to the second entity's decryption key are unable to determine theidentity of patient.

In some embodiments, each entity (or entity group) has their ownencryption key 320 and decryption key 325. For example, each hospital orpractice group may have their own encryption key 320 and decryption key325. As such, medical providers affiliated with the hospital or practicegroup are able to determine the identity of the patient associated withthe medical test package 310 to be able to provide the results andfindings of the test to the patient. In some embodiments, the decryptionkeys 325 are stored in a secure location within client devices of usersthat are authorized to have the decryption keys 325. For instance, thedecryption keys 325 for a health care institution (e.g., a hospital) maybe stored in client devices assigned to medical professionals affiliatedwith the health care institution (e.g., configured by a systemadministrator). Alternatively, the decryption keys 325 are stored in themedical imaging communication system 260 and they are able to beaccessed after a user has been authenticated by the medical imagingcommunication system 260.

In some embodiments, the deidentification module 270 of a first entity(e.g., the deidentification module 270 of an imaging center) accesses anencryption key 320 that is stored in a client device of a second entity(e.g., the client device owned by a hospital), and deidentifies themedical test package 310 using the entity's encryption key 320. Inanother embodiment, the deidentification module 270 locally stores theencryption key 320 for the entity and deidentifies the medical testpackage using the stored encryption key 320.

For example, the deidentification module 270 of a hospital's medicalimaging communication system 260 stores the encryption key 320associated with the hospital. As such, upon receipt of a new medicaltest package 310 for a patient of the hospital, the deidentificationmodule 270 deidentifies the medical test package 310 to generate thedeidentified medical test package 315, and stores the deidentifiedmedical test package 315 in the content store 275.

In some embodiments, the medical imaging device 250 performs thedeidentification prior to sending the medical test packages 310 to themedical imaging communication system 260. For instance, the medicalimaging device 250 may have an encryption key 320 stored therein, or mayretrieve the encryption key 320 from the medical imaging communicationsystem 260.

In yet other embodiments, when the medical image is captured by athird-party entity (e.g., when the patient is referred to a medicalimaging center by the patient's medical provider), the third-partyentity deidentifies the patient's medical test package using thepatient's medical provider's encryption key 320 prior to transmittingthe medical test package 310 to the medical imaging communication system260 of the patient's medical provider. In some embodiment, thethird-party medical imaging center has its own medical imagingcommunication system 260 that is capable of communication with thepatient's medical provider's medical imaging communication system 260.Since the medical imaging center deidentified the medical test package310 using the medical provider's encryption key 320, the medicalprovider is able to decrypt the encrypted portions of the deidentifiedmedical test package 315 to determine the identity of the patient.

When a medical provider 220 accesses the medical imaging communicationsystem 260 to retrieve a patient's medical test package 310, the medicalprovider 220 retrieves the deidentified medical test package 315 fromthe medical imaging communication system 260 and decrypts thedeidentified medical test package 315 using the decryption key 325. Insome embodiments, the decryption of the encrypted portions of thedeidentified medical test package 315 is performed in the medicalprovider's client device 210. In other embodiments, the decryption ofthe encrypted portions of the deidentified medical test package 315 isperformed by the deidentification module 270 of the medical imagingcommunication system 260 after determining that the medical provider isauthorized to access the patient's PII (e.g., by verifying the medicalprovider's credentials or by authenticating an online session of themedical provider).

In some embodiments, the medical provider 220 is able to browse theavailable medical test packages 310 stored in the medical imagingcommunication system 260. Since the medical provider 220 has access tothe decryption key 325, while browsing the available medical testpackages 310, the medical provider 220 is able to retrieve the patients'PII from each of the medical test packages 310.

In some embodiments, collaborators 225 are able to access one or moredeidentified medical test packages 315 stored in the medical imagingcommunication system 260. For instance, a collaborator 225 may be aspecialist asked to provide an opinion on a possible diagnosis for apatient based on the information available in the deidentified medicaltest package 315. In order to provide an opinion, the collaborator 225is able to access the unencrypted portions of the deidentified medicaltest package 315. However, since the collaborator 225 does not have adecryption key 325 corresponding to the encryption key 320 used todeidentify the medical test package 310, the collaborator 225 is unableto decrypt the encrypted portions of the deidentified medical testpackages 315. As such, the collaborator 225 may see the medical testpackage as corresponding to an anonymous patient.

In some embodiments, the collaborator 225 accesses the deidentifiedmedical test packages 315 through the collaborator's medical imagingcommunication system 260. The collaborator's medical imagingcommunication system 260 is able to communicate with the provider'smedical imaging communication system 260 to retrieve the deidentifiedmedical test packages 315 and store the deidentified medical testpackages 315. However, since the collaborator's medical imagingcommunication system 260 does not have the provider's decryption key325, the collaborator's medical imaging communication system 260 isunable to decrypt the encrypted portions of the deidentified medicaltest packages 315.

As such, entities that have access to the decryption key (private keyaccess), such as health professionals within a patient's healthcareinstitution, are able to decrypt the medical test package and access theinformation as if the medical test package was never deidentified.However, entities that do not have access to the decryption key are onlyable to access the deidentified data or the unencrypted portion of thedeidentified medical test package.

By deidentifying the medical test packages as described above, thedeidentified medical test packages can be safely stored or backed up inan external location (e.g., in the cloud), without exposing patient'ssensitive personal identifiable information. That is, prior to pushingthe medical test package to an external storage device, the medical testpackage is deidentified to prevent unauthorized access to patients' PII.Moreover, the deidentification of medical test packages enables medicalprofessionals to collaborate to get opinions and diagnosis withoutsharing a patient's identifiable health data. Additionally, thedescribed deidentification scheme allows the process to be transparentto health professionals that have the clearance or authorization toaccess the patient's identifying information.

In some embodiments, certain fields (such as patient IDs and study IDs)are hashed (e.g., using SHA256) through an irreversible one-waytransformation. This allows studies and patients to be matched byaccessing the deidentified data without having to decrypt the encryptedportion of the deidentified medical test package. That is, this allowsstudies and patients to be matched by simply looking at the unencryptedportion of the deidentified medical test package.

FIG. 4 illustrates a block diagram of the deidentification module 270 ofthe medical imaging communication system 260, according to oneembodiment. The deidentification module 270 includes an authenticationmodule 410, a personal identifiable information (PII) identificationmodule 420, an encryption module 430, and a decryption module 440. Insome embodiments, the deidentification module 270 may include additionalor fewer elements than the ones depicted in FIG. 4 . Moreover, in someembodiments, the elements shown in FIG. 4 may be distributed among morethan one system. For example, the encryption module 430 and thedecryption module 440 may be implemented in different computing systems.

The authentication module 410 authenticates a user of the medicalimaging communication system 260. In some embodiments, theauthentication module 410 authenticates a user based on a username andpassword provided by the user. In other embodiments, the authenticationmodule 410 uses biometric data to authenticate users of the medicalimaging communication system 260. In yet other embodiments, theauthentication module 410 uses multi-factor authentication toauthenticate users of the medical imaging communication system 260. Inyet other embodiments, the authentication module 410 relies on anoperating system level authentication based on the operating system of aclient device connecting to the medical imaging communication system260.

The PII identification module 420 receives a medical test package 310and identifies a patient's PII from the medical test package 310. Insome embodiments, the PII identification module 420 parses through themedical test package 310 and identifies pre-set fields that correspondto PII information. For example, the PII identification module 420identifies a name field as containing a patient's PII.

In some embodiments, the PII identification module 420 uses a set ofrules for determining whether a piece of information is PII. Forexample, the PII identification module determines whether a fieldcontains PII based on the name of the field. For instance, if the nameof the field is “address,” the PII identification module 420 identifiesthe information contained in that field as PII. Alternatively, the PIIidentification module 420 compares the content included in the medicaltest package 310 to the corresponding patient's known PII. If the PIIidentification module 420 finds a match, the PII identification module420 identifies the matching information as PII. For instance, afterscanning the information included in the medical test package 310, thePII identification module 420 determines that the words “Jane Doe”matches the name of the corresponding patient. In this case, the PIIidentification module 420 identifies the words “Jane Doe” as PII.

In some embodiments, the PII identification module 420 additionallyapplies a trained model (in addition to or instead of identifyingpre-set fields) to identify a patient's PII. For example, the PIIidentification module 420 trains a classifier module to identify PIIbased on labeled data that species portions of medical test packages ascontaining a patient's PII.

The encryption module 430 encrypts the identified PII from a medicaltest package 310 to generate a deidentified medical test package 315.The encryption module 430 uses an encryption key 320 to deidentify themedical test package 310. In some embodiments, the deidentificationmodule 270 locally stores the encryption key 320. In other embodiments,the deidentification module 270 requests the encryption key from acomputing device of the user providing the medical test package 310. Inother embodiments, a user providing the medical test package 310 mayselect the encryption key to use for deidentifying a medical testpackage 310. For instance, an operator of a medical imaging device(e.g., from a third-party medical imaging center) may specify to use anencryption key corresponding to the patient's medical provider. As such,the encryption module 430 communicates with the medical imagingcommunication system 260 of the patient's medical provider and retrievesthe patient's medical provider's encryption key 320.

The decryption module 440 decrypts the encrypted portions of adeidentified medical test package 315. The decryption module 440 uses adecryption key 325 to decrypt the encrypted portions of the deidentifiedmedical test package 315. The decryption key 325 is stored locally bythe deidentification module 270 and is kept secret. In some embodiments,the decryption key 325 and the encryption key 320 are the same. In thisembodiment, the encryption key 320 is also stored locally and keptsecret. In other embodiments, the decryption key 325 is different thanthe encryption key 320. Moreover, the decryption key 325 and theencryption key 320 are generated in a way that prevents a third partyfrom deriving one from the other. As such, the encryption key 320 may beshared with third parties to allow third parties to deidentify medicaltest packages using the encryption key 320.

Additionally, the encryption key 320 may be used to authenticate thedeidentified medical test package 314. In particular, thedeidentification module 270 may sign the deidentified medical testpackage 315 using the decryption key 325. The signature included in thedeidentified medical test package 315 can then be verified using thecorresponding encryption key 320.

In some embodiments, the authentication of the deidentified medical testpackages 315 can be used to prevent third parties from tampering withthe information included in the deidentified medical test packages 315.For instance, a malicious third party may attempt to modify anunencrypted portion of the deidentified medical test packages 315 (e.g.,by modifying a medical image included in the deidentified medical testpackages 315). Using the authenticating process, since the third partywill be unable to authenticate ownership of the deidentified medicaltest packages 315, the third party may be prevented from making theunauthorized modification.

Alternatively, the authentication process may be used to detecttampering of the deidentified medical test packages 315. If a thirdparty maliciously modifies the deidentified medical test packages 315,the owner of the deidentified medical test packages 315 may proveownership of the deidentified medical test packages 315 using a digitalsignature and may provide the correct information to revert the changesmade by the third party.

FIG. 5 illustrates a flow diagram of a process for deidentifying medicaltest packages 310, according to one embodiment. The deidentificationmodule 270 receives 510 a medical test package 310. For example, thedeidentification module 270 receives the medical test package 310 fromthe medical imaging device 250. In some embodiments, the medical testpackage 310 is formatted according to a pre-determined standard (e.g.,according to the DICOM standard). The received medical test package 310includes patient's personal identifiable information (PII), patient'snon-identifiable information, and test results (e.g., medical images).

The PII identification module 420 identifies 515 patient PII informationfrom the received medical test package 310. The encryption module 430encrypts 520 the identified patient PII information. The encryptionmodule 430 replaces the patient PII information in the medical testpackage 310 with the encrypted patient PII information to generate thedeidentified medical test package 315. In some embodiments, each PIIfield is independently encrypted and replaced. In other embodiments, themedical test package 310 is split into two portions, one containing allof the patient PII information. In this embodiment, the portion of themedical test package 310 containing all of the patient PII is encryptedas a single block of data.

The deidentified medical test package 315 is stored 530 in the contentstore 275 of the medical imaging communication system 260. As such,entities with access to the content store 275 or the medical imagingcommunication system 260 may retrieve the deidentified medical testpackage 315.

For example, the patient's medical provider 220 retrieves 540 thedeidentified medical test package 315. Since the medical provider 220has access to the decryption key 325 (e.g., either by retrieving thedecryption key 325 from a secure location within the client device 210of the medical provider 220, or by having authorization to use thedecryption key 325 through the medical imaging communication system 260after having been authenticated by the medical imaging communicationsystem 260), the medical provider 220 is able to decrypt thedeidentified medical test package 315. Thus, the medical providerdecrypts 545 the deidentified medical test package 315 using thedecryption key 325 and displays 550 the unencrypted portion of themedical test package 310 (e.g., test information and test results) aswell as the decrypted patient PII information.

In another example, a collaborator 225 retrieves 570 the deidentifiedmedical test package 315. Since the collaborator 225 does not have thedecryption key 325, the collaborator is unable to decrypt thedeidentified medical test package 315. As such, the client device 210Bof the collaborator 225 displays 580 the unencrypted portion of themedical test package 310 (e.g., test information and test results) andattributes the deidentified medical test package 315 to an anonymouspatient.

In some embodiments, a client device determines if the decryption key325 is available by attempting to decrypt the encrypted portion of thedeidentified medical test package 315. If the decryption succeeds, theclient device determines that the decryption key 325 is available.However, if the decryption is not successful, the client devicedetermines that the decryption key 325 is not available.

The deidentification scheme described above enables enhances thesecurity and privacy of the systems handling data including patients'PII, as well as enables various entities to share patients' medicalinformation without compromising the security and privacy of thepatients.

For example, by deidentifying the medical test package before beingstored, a patient's PII is not accessible to an entity withoutpossession or access to the decryption key associated with theencryption key used for deidentifying the medical test package.Moreover, this added security layer is transparent for entities that dohave access to the decryption key as the decryption of the patients' PIIcan be performed in the background each time the entity with decryptionkey access attempts to retrieve a medical test package.

Additionally, by deidentifying the medical test packages as describedabove, a medical test package can be shared with third-party entitiesthat do not have access to the decryption key associated with theencryption key used for deidentifying the medical test package, whilemaintaining the third-party entities' ability to view information useful(e.g., digital medical images) for making a diagnosis or recommendationfor a particular patient, or for conducting large scale medical studies.

B. Volumetric Imaging

The volumetric imaging module 280 receives a request, from a clientdevice, for a new volumetric image and provides a set of images togenerate the volumetric image. For example, the volumetric imagingmodule 280 provides a set of slice images taken across a predeterminedplane (e.g., the transverse plane, the sagittal plane, or the frontalplane) to enable the client device to generate a three-dimensional (3D)model using the set of slice images.

In some embodiments, the reduce the amount of data being transmitted tothe client device, the set of slice images are compressed prior to beingtransmitted to the client device. Moreover, to preserve the imagequality of the set of slice images, a lossless compression algorithm maybe used. Moreover, to increase the efficiency of the compressionalgorithm, two or more slices are combined into a combined image priorto applying the image compression algorithm.

FIGS. 6A and 6B illustrate system environments for the compression of aset of slice images, according to various embodiments. FIG. 6Cillustrates an example set of slice images, according to one embodiment.The system environment of FIGS. 6A and 6B show a set of slice imagescontaining eight images A through H. A first subset of images (e.g.,slices A through D) are combined into a first combined image 620A and asecond subset of images (e.g., E through H) are combined into a secondcombined image 620B.

In some embodiments, the number of slices that are combined into asingle combined image is determined based on the size of each slice andthe capabilities of the client device. For example, the client devicemight be limited on a maximum image size that it can handle (e.g., basedon computational power, available memory, or operating systemrestrictions). For instance, if the image size limit of the clientdevice is 4096×4096 pixels and each slice is 512×512 pixels, then 64slices may be combined into a single combined image 620. As such, if theset of slice images include 320 slices (each having a resolution of512×512 pixels), those slices can be combined into 5 combined images620.

In some embodiments, as shown in the example of FIG. 6A, the lines orpixels from each of the images used for generating a single combinedimage are interlaced. For example, to improve the efficiency of thecompression, since adjacent slices are similar to each other, the pixelsof two or more images are interlaced to reduce the number of sharpchanges in the combined image.

Alternatively, as shown in the example of FIG. 6B, to improve the speedof the process, each image is spread horizontally in the combined image620. For example, if each slice is 512 pixel wide and the combined imageis 4096 pixels wide, the pixels in the first 8 rows of the first sliceare placed in the first line of the combined image, the second 8 rows ofthe first slice are placed in the second line of the combined image, andso forth. After all the pixels of the first slice are placed in thecombined image, the pixels of the second slice are placed in the placedin the combined image. As such, the combined image can be composedsequentially based on the data for each slice independently.

In some embodiments, to generate a combined image 620, a blank imagehaving a pre-determined size is generated. The pre-determined size maybe determined based on the number of slices 610 to be combined and thesize of each of the slices 610. Alternatively, the pre-determined sizemay be a pre-set value. After the blank image is initialized, the pixelsof the blank image are modified based on the pixel value of acorresponding pixel of a corresponding slice.

For example, for each pixel of each slice 610 in a subset of slices tobe combined into a combined image 620, a corresponding location in thecombined image is determined. Then, the pixel value of the blank imageat the determined location is modified to match the pixel value of thepixel of the slice. Alternatively, if the bit depth (or color depth) ofthe combined image is different than the bit depth of the slice, thepixel value of the blank image at the determined location is modifybased on a subset of bits of the pixel of the slice.

After the slices have been combined into combined images, the combinedimages are compressed to reduce the amount of data being transferred tothe client device. In some embodiments, the compressed combined imagesare stored in the content store 275. In other embodiments, thecomposition of the combined images and the compression of the combinedimages is performed each time a new request is received from the clientdevice.

In some embodiments, the images are decomposed into multiple resolutionplanes. For example, each slice may be received as having a 16-bit grayscale color depth. Each slice may be split into two 8-bit gray scalecolor depth images. The slices can then be combined into two (or more)sets of combined images, each corresponding to resolution plane ofslices.

FIG. 7A illustrates a system environment for the decompression of a setof slice images, according to one embodiment. The system environment ofFIG. 7A shows a set of compressed images 710. A first compressed image710A is then decompressed to obtain a first combined image 720A.Similarly, a second compressed image 710B is decompressed to obtain asecond combined image 720B. Although the example embodiment of FIG. 7Aonly shows two compressed images 710, additional compressed images maybe used depending on the number of slices captured by the medicalimaging device.

After the compressed images 710 are decompressed, the decompressedcombined images 720 are split to generate a set of slices 730. Forexample, the first decompressed combined image 720A is split into afirst subset of slices and the second decompressed combined image 720Bis split into a second subset of slices. In some embodiment, additionalmetadata is provided to specify a set of parameters for splitting thecombined images. For example, the set of parameters may include the sizeof each of the slices to extract from the combined images, or a schemeused to combine the slices to generate the combined image.

In some embodiments, to split each of the decompressed combined images,a set of blank images are first initialized. Then, for each initializedblank image, the pixel values of each of the pixels are determined fromone or more decompressed combined images 720. After determining thepixel values, the initialized blank images are modified to store thedetermined pixel values at predetermined locations of the initializedblank image.

In some embodiments, if the bit depth (or color depth) of thedecompressed combined image is the same as the set of slices to beextracted, the pixel values for each pixel of a slice is determined froma single decompressed combined image. In other embodiments, if the bitdepth of the decompressed combined image is lower than the set of slicesto be extracted, the pixel value for each pixel of a slice is determinedfrom multiple decompressed combined image.

FIG. 7B illustrates a system environment for the generating athree-dimensional (3D) model from a set of slides, according to oneembodiment. After the slices are extracted from the combined images, theslices are used to generate a texture map 750. The texture map mayinclude one or more textures 760 that is used by a shader to providehigh frequency details to the surface of the 3D model.

In some embodiments, the model is also generated from the extractedslices. For example, based on the image in each of the slices, a 3D meshstructure 770 is generated. In some embodiments, vertices for the 3Dmesh structure are identified from each of the slices and the verticesare connected to form the 3D mesh structure. The 3D mesh and the texturemap are then used to render an image in a display of a client device.

In some embodiments, the client device allows users to interact with the3D model. For example, the client device allows users to pan, rotate,and zoom the 3D model to enable the user to view the 3D model fromdifferent angles and at different levels of detail (LOD). In someembodiments, multiple models at different LODs are generated. Then whenrendering an image for displaying to the user, one of the models isselected based on the angle and zoom parameters used for the rendering.For instance, two models, one with a fine meshing, and one with a coarsemeshing are generated. When the client device is rendering, one of thetwo models is selected based on the zoom parameters to either speed upthe rendering process (e.g., by using the coarse meshing) or to provideadditional levels of details to the user (e.g., by using the finemeshing).

FIG. 8 illustrates a block diagram of the volumetric imaging module 280of the medical imaging communication system 260 in communication with avolumetric imaging client 850 of a client device 210, according to oneembodiment. The volumetric imaging module 280 includes an imagecombination module 810 and an image compression module 820. Thevolumetric imaging module 280 communicates with a volumetric imagingclient 850 of a client device 210. The volumetric imaging client 850includes an image decompression module 860, a slice extraction module865, a texture mapping module 870, a mesh generation module 875, and a3D shader 880. Moreover, the client device 210 includes a display device890 and an input device 895.

The image combination module 810 receives a set of slice images (slices)610 and generates one or more combined images 620. The image combinationmodule 810 selects a subset of slices 610 to combined into a singlecombined image 620 and arranges the pixels of each of the slices 610 inthe subset of slices in the combined image 620.

The image compression module 820 receives one or more combined images620 and compresses the received combined images. In some embodiments,the image compression module 820 uses a lossless compression algorithm.In some embodiments, the combination of slices by the image combinationmodule 810 is performed in a manner to increase the efficiency of theimage compression algorithm used to compress the images. As such, thefile size of the compressed combined image is lower than the combinedfile sizes of the slice images in the subset of slices.

The image decompression module 860 receives one or more compressedcombined images 710 and decompresses the compressed combined images torestore the combined images 720. In some embodiments, the imagedecompression module 860 identifies a compression algorithm used tocompress the combined image and applies an inverse operation to thecompression algorithm.

The slice extraction module 865 receives a combined image 720 andextracts the slices 730 used to compose the combined image 720. In someembodiments, the slice extraction module 865 identifies a set ofparameters for performing the extraction of the slices. For example, theslice extraction module 865 determines a size (e.g., in pixel) of eachslice, a number of slices, and an algorithm used to combine the slices.

The texture mapping module 870 receives a set of slices and generates atexture map 750 for rendering a 3D model of the object depicted in theslices. The texture map 750 may include multiple textures 760 that theshader 880 can use to render images of the 3D model. In someembodiments, the texture mapping module 870 generates a set of matricesor vectors based on the pixel information stored in each of the slices.For example, for each slice, the texture mapping module 870 generates aset of vectors that stores the value for each of the pixels of the slicein a specific location within a specific vector of the set of vectors.Alternatively, each vector of the set of vectors generated by thetexture mapping module 870 is generated using values for pixels inmultiple slices. That is, each vector element in a specific vector maycorrespond to a pixel for each of a set of slices at a specific locationwithin the corresponding slice.

For example, based on the size of each slice and the number of slices inthe set of slices, the texture mapping module initializes a set numberof vectors, each having a predetermined size. In some embodiments, thesize of the vector is further based on a capability of the computingdevice executing the texture mapping module 870. For example, thetexture mapping module 870 may choose a vector size that is based on themaximum natively supported vector size for the computing device (e.g.,based on the capabilities of a graphics processor or the capabilities ofthe operating system of the computing device). For each vector elementof each vector, the texture mapping module 870 stores a valuecorresponding to a pixel of a specific slice at a specific location.

The mesh generation module 875 receives the set of slices and generatesa 3D mesh 770 for rending a 3D model of the object depicted in theslices. In some embodiments, the mesh generation module 875 determinesthe position of multiple vertices based on each of the slices in the setof slices and connect the vertices to form the 3D mesh. For instance,the mesh generation module 875 may identify edges in each of the slicesto determine the position of various surfaces. The mesh generationmodule 875 then inserts a vertex to the mesh based on the identifiedlocation of a surface. Moreover, the mesh generation module 875 connectstwo or more vertices together to form the 3D mesh. For instance, themesh generation module 875 connects vertices to form triangles to definethe surfaces identified based on the set of slices.

The 3D shader 880 receives the 3D mesh 770 and the texture map 750 andrenders the model for display to the user via the display device 790.That is, based on a set of render parameters (e.g., angle, zoom,resolution) the 3D shader 880 generates a 2D image representation of the3D model and sends the generated 2D image to the display device 790 ofthe client device 210. In some embodiments, the 3D shader 880 is given acertain budget (e.g., a computational budget and a time budget) and the3D shader 880 generates the 2D image within the provided budget.

In some embodiments, the rendering parameters are changed based on userinput received though the input device 895. The user input may be atouch input on a touch sensitive display, or a pointer input (e.g., amouse input) using a peripheral connected to the client device. The userinput may indicate to change the rendering angle, the rendering zoom, orthe resolution of the rendered image. The 3D shader 880 receives theuser input or the change in rendering parameters and re-renders the 3Dmodel based on the new rendering parameters.

In some embodiments, the 3D shader 880 generates the 2D imagerepresentation of the 3D model without using a 3D mesh. That is, basedon the texture map generated by the texture mapping module 870 ordirectly from the set of slices, the 3D shader 880 determines the 2Dimage corresponding to a set of rendering parameters. For example, basedon a rendering angle, for each pixel of the 2D image, the 3D shader 880identifies a set of pixels from the texture map or the set of slices anddetermines a new pixel value by manipulating the values of theidentified pixels. If the texture map is a set of vectors or matrices,the 3D shader 880 may manipulate the vectors or matrices of the texturemap to generate the 2D image representation of the 3D model at thespecified rendering angle.

FIG. 9 illustrates a flow diagram of a process for rendering a3-dimensional image from a set of slice images, according to oneembodiment. The volumetric imaging module 280 receives 910 a set ofslices. In some embodiments, the set of slices are stored in the contentstore 275. For example, the set of slices may be extracted fromdeidentified medical test packages 315. In other embodiments, the set ofslices are received from other sources, such as a medical imaging device250 or a third-party online repository.

The image combination module 810 combines 915 the set of slices togenerate one or more combined images 620. In some embodiments, the setof combined images are split into multiple subsets of slices and eachsubset is combined into a single combined image. In other embodiments,each subset is combined into multiple combined images, each containingportions of every slice in the subset of slices. For example, if eachslice uses a 16-bit color depth, each combined image may be generatedwith one half (8 bits) of the color information for each slice in thesubset of slices. That is, a first combined image may include the first8 bits of color information for each slice in the subset of slices, anda second combined image may include the second 8 bits of the colorinformation for each slice in the subset of slices.

The one or more combined images 620 are compressed 920 to generatecompressed images 710. In some embodiments, the combined images arecompressed using a lossless compression algorithm to prevent any data orresolution loss during the compression process. The compressed images710 are then sent 925 to a client device 210 (e.g., a provider's 220client device 210A or a collaborator's 225 client device 210B).

The client device 210 receives 930 the compressed images 710. The imagedecompression module 860 then decompresses 935 the compressed images710, and the slice extraction module 865 extracts 940 the set of slicesfrom the uncompressed combined images 720. As such, the informationcontained in the initial set of slices is transferred from the medicalimaging communication system 260 to the client device 210 to a clientdevice (e.g., a mobile device such as a smartphone or a tablet) whilereducing the amount of data and bandwidth employed to transfer the setof slices without any loss of information. Moreover, the compressedimages 710 enable the set of slices to be transferred between devicesfaster, increases accessibility and portability of the set of slices.

The texture mapping module 870 generates 945 a texture map 750 from theextracted set of slices. The texture map includes one or more texturesfor rendering a 3D model of the object depicted in the set of slices. Insome embodiments, the mesh generation module 875 generates a 3D mesh 770from the extracted set of slices. In other embodiments, a pre-generated3D mesh is selected based on an analysis of the set of slices. In yetother embodiments, the 3D mesh is provided to the client device 210 bythe medical imaging communication system 260. In yet other embodiments,a default 3D mesh (e.g., a cylinder, a sphere or a spheroid, a cube, orany other polyhedron or combination thereof) may be used to render the3D model.

Using the texture map 750 and the 3D mesh 770, the 3D shader 880,renders 950 the 3D model and generates a 2D image for display through adisplay device 890 of the client device 210. In some embodiments, theshader renders the 3D model based on a set of parameters (e.g., zoom,angle, resolution). Moreover, the user may change the renderingparameters using an input device 895. For instance, the user mayinteract with the displayed image to rotate the 3D model. Based on theuser's interaction, the rendering parameters are modified, and the 3Dmodel is re-rendered. In some embodiments, the rendering parameters areupdated in a predetermined way. For instance, the rendering anglechanges to display a rotating 3D model to the user.

In some embodiments, the 3D model is periodically re-rendered (e.g., 30or 60 times per second). In other embodiments, client device executes arendering loop, where a new rendering cycle starts after a priorrendering cycle ends. In other embodiments, the client device determinesif any of the rendering parameters has changed and starts a newrendering iteration when a change in the rendering parameters isdetected.

In some embodiments, in addition to rendering a 3D model, the volumetricimaging client 850 is capable of generating slices in a second planefrom the slices in a first plane. That is, using the set of slices takenacross the first plane (e.g., the transverse plane), the volumetricimaging client is able to determine how slices in any other plane (e.g.,the frontal plane or the sagittal plane) would look like. In someembodiments, the new slices are generated directly from the set ofslices in the first plane. In other embodiments, the new slices aregenerated from the 3D model. That is, the new slices are generating byslicing the 3D model into 2D images that are a set distance apart fromeach other.

In yet other embodiments, the new slices are generated by calculatingnew pixel values for each of the new slices based on the availableslices. That is, for each pixel in a new slice corresponding to thesecond plane, a set of pixels in the available slices corresponding tothe first plane are identified. The volumetric imaging client 850 thencomputes a new pixel value based on the identified set of pixels fromthe available slices corresponding to the first plane. For example, foreach pixel in a new slice corresponding to a second plane at a setdepth, 8 or 9 pixels are identified from one or more slices in theavailable slices corresponding to the second plane. Based on theidentified 8 or 9 pixels, the volumetric imaging client 850 determinesthe value for the new pixel. This is repeated for each pixel of the newslice. In some embodiments the available slices are stored in one ormore matrices or vectors and the new set of slices are generated bymanipulating the matrices or vectors based on the angle at which theavailable slices are being rotated to transform the set of slices fromthe first plane to the second plane.

For instance, based on the pixel values for each of the slices, a 3Dmatrix may be generated. Then, based on the 3D matrix, other slices inother plans may be generated. That is, a matrix M[x][y][z] is generatedusing each of the slices in the set of slices. The matrix is populatedby copying the pixel values for a slice into a corresponding plane inthe matrix M. That is, the first slice is copied into the matrixelements where z=0, the second slice is copied into the matrix elementswhere z=1 and so on. Then, a first slice in the sagittal plane isgenerated by extracting 2D matrices where x=1, a second slice in thesagittal plane is generated by extracting 2D matrices where x=2, and soon. Additionally, a first slice in the frontal plane is generated byextracting 2D matrices where y=1, a second slice in the frontal plane isgenerated by extracting 2D matrices where y=2, and so on.

Alternatively, the slices in other planes are generated by extractingrows or columns of pixels from each of the slices in the set of slicesat a predetermined position. For example, a first slice in the sagittalplane is generated by the first column of pixels in each slice in theset of slices, a second slice in the sagittal plane is generated by thesecond column of pixels in each slice in the set of slices, and so on.Moreover, a first slice in the frontal plane is generated by the firstrow of pixels in each slice in the set of slices, a second slice in thefrontal plane is generated by the second row of pixels in each slice inthe set of slices, and so on.

In some embodiments, interpolation algorithms are used to determinepixel values corresponding to pixels that are in between two slices.Additionally, the interpolation algorithms may be used to smooth out thegenerated set of slices in across different planes.

By compressing the set of slices as described above, an amount of datato be transferred to a client device to enable the client device torender a 3D model of an object depicted in the slices may be reduced.That is, the amount of data to be sent using the described compressionalgorithm may be smaller compared to sending a pre-generated 3D modeland a pre-generated 3D texture map. In particular, the benefit oftransmitting the compressed data as described above increases when theinformation is to be sent without loss of fidelity. That is, without theuse of lossy compression algorithms that can reduce the resolution andfidelity of the image to be displayed by the client device. This benefitis particularly useful when transferring sets of slices to a mobiledevice via a wireless connection that might get bottlenecked by thelarge amount of data to be transferred.

Moreover, by sending the set of slices from which a 3D can be generated,as opposed to sending the pre-generated 3D model, the client device isable to provide additional functionality without any additional costassociated with data transfer. For example, the client device is able togenerate multiple models with varying levels of detail, generateadditional slices across different planes, etc.

C. Visual Reporting

The visual reporting module 285 receives a medical test package 310 or adeidentified medical test package 315 and formats the contents fordisplay by a client device 210. For example, the visual reporting module285 generates a web page to display the contents of the medical testpackage. The visual reporting module 285 then sends the formattedcontent to a client device for display (e.g., through a web browserapplication).

FIG. 10A illustrates a system environment for processing digital medicalimages in a collaborative way, according to one embodiment. FIG. 10Aillustrates a medical image 1010 and a report 1030 for the medicalimage. The medical image depicted in FIG. 10A corresponds to an X-Ray ofthe left hand of a patient. However, any other suitable medical image(such as a CT scan, an MRI, or an ultrasound) may also be used. Thereport 1030 is a radiology report provided by a medical professionalafter inspecting the medical image 1010. In some embodiments, the report1030 is provided by a collaborator 225.

The report 1030 includes a description of what the medical professionalobserved in the medical image. For example, the report 1030 includes afinding 1040 and a location 1045 that corresponds to the finding 1040.In some embodiments, the report 1030 may be automatically generated bythe visual reporting module 285 and edited by a medical professional toaugment or correct the findings of the visual reporting module 285. Inthe example of FIG. 10A, the finding is the presence of a small foreignobject and the location of the finding is the distal interphalangealjoint (DIP) of the second digit.

In some embodiments, natural language processing (NLP) is used toidentify the findings and the locations embedded in the report 1030. Insome embodiment, the NLP identifies words related to locations and nounscorresponding to parts of a human body. In other embodiments, themedical professional also provides a location associated with thereport. The location is stored in conjunction with the report. Forexample, the medical professional may specify a point or coordinate(e.g., by clicking a specific point in the medical image), or an area(e.g., by drawing a bounding box or area on the medical image). Thevisual reporting module 285 then stores the provided location fordisplay to a viewing user.

In some embodiments, based on the description of the location, thevisual reporting module 285 finds a coordinate or area on the medicalimage corresponding to the location 1020. For example, if the location1045 is “adjacent to the DIP of the 2nd digit,” an image recognitionmodel identifies a region of the medical image corresponding to thespecified location. In some embodiments, a trained model is used toidentify the specified location. For example, a classifier model thatscans the medical image using a set of “windows” and determines if anyof the windows have a likelihood greater than a threshold value ofdepicting the specified location.

FIG. 10B illustrates a user interface for viewing a medical report 1030corresponding to a medical image 1010, according to one embodiment. Theuser interface shows the medical image 1010 and the medical report 1030.The medical image is modified to include an identification 1060 of thefinding 1040 at the specified location 1045. Additionally, the userinterface may include buttons 1070 to interact with the medical image orthe report. For instance, the buttons allow a viewing user to zoom in orout the medical image, or to change the contrast and saturation of themedical image. Moreover, the buttons may allow a viewing user to modifythe medical report 1030 or to change the identification 1060 of thelocation if the system incorrectly determined position corresponding tothe location.

In some embodiments, prior to displaying the medical image, the visualreporting module 285 identifies bounding boxes to the medical images andcorrects a rotation of the medical images. Moreover, the visualreporting module 285 may equalize the size of multiple medical images topresent the medical images as having similar dimensions.

FIG. 11 illustrates a block diagram of the visual reporting module ofthe medical imaging communication system, according to one embodiment.The visual reporting module 285 includes a natural language processing(NLP) module 1110, an image recognition module 1115, an imagecomposition module 1120, a formatting module 1125, a learning module1130, and a web server 1140.

The NLP module 1110 analyzes written medical reports and extracts one ormore pieces of information (e.g., data) from the medical reports. Insome embodiments, the NLP module extracts at least a description of alocation from the text of the medical reports. The NLP module 1110 usesone or more NLP models to extract the one or more pieces of information.For instance, the NLP module 1110 may include a different model for eachtype of information to be extracted from the text of the medical report.That is, the NLP module 1110 may include a first NLP model that extractsa description of a location from the text of the medical report, and asecond NLP model that extracts a description of the finding of themedical report.

The NLP models of the NLP module 1110 are trained by the learning module1130. The NLP models may be trained using labeled training data. Forinstance, the NLP models are trained using medical reports for which thepieces of information to be extracted are manually identified. In someembodiments, the NLP models are updated using user feedback regardingthe accuracy of the NLP models. For instance, a user may provide anindication that an output of an NLP model is inaccurate (and provides acorrect output for the model). The learning module 1130 may thenre-train the NLP model based on the received feedback.

The image recognition module 1115 receives a medical image andidentifies one or more objects in the received medical image. In someembodiments, the image recognition module 1115 identifies as manyobjects as possible. In other embodiments, the image recognition module1115 identifies one or more specified objects. That is, for each regionof the medical image, the image recognition module 1115 determines ifthe specified object is present or not.

In some embodiment, the image recognition module 1115 uses a rollingwindow that scans across the medical image. The image recognition module1115 starts with a window having a predetermined size and determines ifan object is present in the window. After analyzing the window, theimage recognition module 1115 moves the window and repeats the analysiswith the new window. This process is repeated until the entire image hasbeen analyzed. In some embodiments, after the entire image has beenanalyzed, the size of the window is modified, and the process isrepeated with the new window size.

The image recognition module 1115 uses one or more models to identifyobjects. For example, the image recognition module 1115 may include aseparate model to identify each of the objects that are supported by theimage recognition module 1115. Each image recognition module may betrained using positive training sets including the object to berecognized. Moreover, each model may provide a score indicative of alikelihood that the object being identified is present. The imagerecognition module 1115 then compares the output of a model to athreshold value to determine if the object is present.

In some embodiments, the image recognition module 1115 selects a modelto apply based on the object to be identified. For instance, if thespecified object to be identified is a “DIP of the 2nd digit,” the imagerecognition module 1115 selects a model for identifying interphalangealjoints. Moreover, the image recognition module 1115 may use multiplemodels to identify a single object. For example, to identify the “DIP ofthe 2nd digit,” the image recognition module 1115 may use a first imagerecognition model to identify the second digit of a patient in a medicalimage, and a second image recognition model to identify aninterphalangeal joint within the identified portion of the imagecorresponding to the second digit of the patient.

Additionally, the image recognition module 1115 analyzes medical imagesand determines a likelihood that one or more abnormalities are presentin the medical image. For example, the image recognition module 1115 maybe trained to detect foreign objects that are not expected to be presentin a specific medical image. The image recognition module 1115 may traina model for each abnormality supported by the image recognition module1115. Alternatively, the image recognition module 1115 trains a singlemodel that is able to recognize multiple types of abnormalities in amedical image. In some embodiments, the image recognition module 1115identifies a type of object depicted in the medical image and selects amodel to apply to the medical image based on the identified object. Forinstance, if the identified object is a heart, the image recognitionmodule 1115 applies one or more abnormality detection model that isspecific for heart images. Conversely, if the identified object is alung, the image recognition module 1115 applies one or more abnormalitydetection model that is specific for lung images.

The image composition module 1120 receives a medical image, and anidentified area corresponding to a medical report, and modifies theimage to include an indication of the area corresponding to the medicalreport. For instance, the image composition module 1120 adds a boxsurrounding the identified area corresponding to the medical report.Moreover, the image composition module 1120 adds a short description ofthe medical report near the indication of the area corresponding to themedical report.

In some embodiments, the image composition module 1120 additionallymodifies the medical image to correct certain properties of the medicalimage. For instance, the image composition module 1120 may rotate themedical image, may change the saturation and brightness of the medicalimage, or may change the size or resolution of the medical image.

The formatting module 1125 generates a web page to present a medicaltest package to a viewing user. That is, formatting module 1125generates a web page to present the medical image and the medical reportto a viewing user. In some embodiments, the formatting module 1125receives an indication of a user's credentials and determines whether todisplay a patient's PII or to display the medical test package as beingattributed to an anonymous patient. For example, the formatting module1125 includes the patient's PII if the viewing user is the medicalprovider 220 of the patient 230, and provides the medical test packageas being attributed to an anonymous patient if the viewing user is acollaborator 225.

The learning module 1130 applies machine learning techniques to generateone or more models that when applied to content items output indicationsof whether the content items have the associated property or properties,such as probabilities that the content items have a particular Booleanproperty, or an estimated value of a scalar property. As used herein,content items include any type of content, such as medical images ormedical reports.

As part of the generation of the models, the learning module 1130 formsa training set of content items by identifying a positive training setof content items that have been determined to have the property inquestion, and, in some embodiments, forms a negative training set ofcontent items that lack the property in question.

The learning module 1130 extracts feature values from the content itemsof the training set, the features being variables deemed potentiallyrelevant to whether or not the content items have the associatedproperty or properties. An ordered list of the features for a contentitem is herein referred to as the feature vector for the content item.In one embodiment, the learning module 1130 applies dimensionalityreduction (e.g., via linear discriminant analysis (LDA), principlecomponent analysis (PCA), or the like) to reduce the amount of data inthe feature vectors for content items to a smaller, more representativeset of data.

The learning module 1130 uses supervised machine learning to train themodels, with the feature vectors of the positive training set and thenegative training set serving as the inputs. Different machine learningtechniques—such as linear support vector machine (linear SVM), boostingfor other algorithms (e.g., AdaBoost), neural networks, logisticregression, naïve Bayes, memory-based learning, random forests, baggedtrees, decision trees, boosted trees, or boosted stumps—may be used indifferent embodiments. The models, when applied to the feature vectorextracted from a content item, outputs an indication of whether thecontent item has the property in question, such as a Boolean yes/noestimate, or a scalar value representing a probability.

In some embodiments, a validation set is formed of additional contentitems, other than those in the training sets, which have already beendetermined to have or to lack the property in question. The learningmodule 1130 applies the trained model to the content items of thevalidation set to quantify the accuracy of the models. Common metricsapplied in accuracy measurement include: Precision=TP/(TP+FP) andRecall=TP/(TP+FN), where precision is how many the models correctlypredicted (TP or true positives) out of the total it predicted (TP+FP orfalse positives), and recall is how many the models correctly predicted(TP) out of the total number of content items that did have the propertyin question (TP+FN or false negatives). The F score (F-score=2*PR/(P+R))unifies precision and recall into a single measure. In one embodiment,the learning module 1130 iteratively re-trains the models until theoccurrence of a stopping condition, such as the accuracy measurementindication that the model is sufficiently accurate, or a number oftraining rounds having taken place.

The web server 1140 links the visual reporting module 285 via thenetwork 220 to the one or more client devices 210. The web server 1140serves web pages, as well as other content, such as JAVA®, FLASH®, XMLand so forth. In some embodiment, the content provided by the web serveris provided by the formatting module 1125 in response to a requestreceived from a client device 210. The web server 1140 may receive androute messages between the visual reporting module 285 and the clientdevice 210. A user may send a request to the web server 1140 to uploadinformation (e.g., comments for a medical test package) that are storedin the content store 275. Additionally, the web server 1140 may provideapplication programming interface (API) functionality to send datadirectly to native client device operating systems, such as IOS®,ANDROID™, or BlackberryOS.

The client device 210 may executes a web browser application 1160 toenable interaction between the client device 210 and the visualreporting module 285 via the network 220. Alternatively, a client device210 interacts with the visual reporting module 285 through anapplication programming interface (API) running on a native operatingsystem of the client device 210, such as IOS® or ANDROID™. The webbrowser 1160 receives the web pages provided by the web server 1140,interprets the web pages, and causes the web pages to be displayedthrough a display device of the client device 210.

FIG. 12 illustrates a flow diagram of a process for providingcollaborative reports of a digital medical image, according to oneembodiment. The visual reporting module 285 receives 1210 a medicalimage. Additionally, the visual reporting module 285 receives 1215 amedical report for the medical image. In some embodiments, the medicalimage and the medical report are stored in the content store 275 of themedical imaging communication system 260.

The NLP module 1110 identifies one or more pieces of information formthe received medical report. For example, the NPL module 1110 identifies1220, from the received medical report, a description of a locationwithin the medical image. In some embodiments, the NLP module 1110further identifies, from the received medical report, a findingassociated with the identified description of the location.

The image recognition module 1115 identifies 1225 a portion of themedical image corresponding to the identified description of thelocation. In some embodiments, the image recognition module 1115 selectsan image recognition model based on the identified description of thelocation and applies the image recognition model to the medical image toidentify a portion of the image corresponding to the identifieddescription of the location.

The image composition module 1120 overlays 1230 an indication of thelocation in the identified portion of the medical image. For example,the image composition module 1120 overlays a bounding box identifyingthe portion of the medical image corresponding to description of thelocation included in the medical report.

The formatting module 1125 generates 1235 a page for presenting themedical test package and sends the generated page to a viewing user. Thegenerated page includes the medical image generated by the imagecomposition module 1120 and the text of the medical report.

This process enhances the presentation of digital medical images and theresult of an analysis performed to the digital medical images. Byannotating the images based on the analysis of the report for the image,a viewing user is able to more easily understand the meaning orsignificance of the report. Moreover, the annotation process describesabove enables a system to store the original digital medical imagewithout having to store multiple versions of the same digital medicalimage. That is, the system is able to provide the annotated imageswithout having to store a version of the digital medical image that hasthe annotation baked into it.

FIG. 13 illustrates a flow diagram of a process for providing anautomated analysis of medical images, according to one embodiment. Thevisual reporting module 285 receives 1310 a medical image. Additionally,the visual reporting module 285 may receive 1315 a medical report forthe medical image prepared by a medical professional.

The image recognition module 1115 analyzes 1320 the received image todetermine a likelihood that the received image shows an abnormality. Insome embodiments, the image recognition module 1115 first identifies atype of object depicted in the received medical image, and applies oneor more models corresponding to the identified type of object. Forinstance, each model applied may correspond to a type of abnormalitythat is applicable to the identified type of object.

In some embodiments, the image recognition module 1115 analyzes thereceived image based on the contents of the received medical report (ifone is received). For example, the received medical report may identifythe type of object and a region that is suspected to contain anabnormality. As such, the image recognition module 1115 applies one ormore models based on the contents of the medical report. Alternatively,the medical report may indicate a suspected abnormality and the imagerecognition module 1115 applies a corresponding model to determine alikelihood that the suspected abnormality (or a related abnormality) ispresent in the medical image.

To configure the analysis of the image recognition module 1115 based onthe contents of the medical report, the medical report is analyzed usingthe NLP module 1110. The NLP module 1110 extracts relevant informationfrom the received medical report and provides the extracted informationto the image recognition module 1115.

Based on the analysis of the received medical image, an automated reportis generated and provided 1325 to a medical profession. The automatedreport identifies any abnormality having a likelihood higher than athreshold value. In some embodiments, the threshold value is set by themedical professional.

In some embodiments, feedback is received 1330 from the medicalprofessional after the automated report is presented to the medicalprofessional. For example, the medical professional may edit theautomated report, may mark portions of the automated report asinaccurate, or may approve the automated report. Based on the feedbackreceived from the medical professional, one or more models used foranalyzing the medical image are re-trained and updated.

Additional Configuration Consideration

Throughout this specification, plural instances may implementcomponents, operations, or structures described as a single instance.Although individual operations of one or more methods are illustratedand described as separate operations, one or more of the individualoperations may be performed concurrently, and nothing requires that theoperations be performed in the order illustrated. Structures andfunctionality presented as separate components in example configurationsmay be implemented as a combined structure or component. Similarly,structures and functionality presented as a single component may beimplemented as separate components. These and other variations,modifications, additions, and improvements fall within the scope of thesubject matter herein.

Certain embodiments are described herein as including logic or a numberof components, modules, or mechanisms. Modules may constitute eithersoftware modules (e.g., code embodied on a machine-readable medium or ina transmission signal) or hardware modules. A hardware module istangible unit capable of performing certain operations and may beconfigured or arranged in a certain manner. In example embodiments, oneor more computer systems (e.g., a standalone, client or server computersystem) or one or more hardware modules of a computer system (e.g., aprocessor or a group of processors) may be configured by software (e.g.,an application or application portion) as a hardware module thatoperates to perform certain operations as described herein.

In various embodiments, a hardware module may be implementedmechanically or electronically. For example, a hardware module maycomprise dedicated circuitry or logic that is permanently configured(e.g., as a special-purpose processor, such as a field programmable gatearray (FPGA) or an application-specific integrated circuit (ASIC)) toperform certain operations. A hardware module may also compriseprogrammable logic or circuitry (e.g., as encompassed within ageneral-purpose processor or other programmable processor) that istemporarily configured by software to perform certain operations. Itwill be appreciated that the decision to implement a hardware modulemechanically, in dedicated and permanently configured circuitry, or intemporarily configured circuitry (e.g., configured by software) may bedriven by cost and time considerations.

Accordingly, the term “hardware module” should be understood toencompass a tangible entity, be that an entity that is physicallyconstructed, permanently configured (e.g., hardwired), or temporarilyconfigured (e.g., programmed) to operate in a certain manner or toperform certain operations described herein. As used herein,“hardware-implemented module” refers to a hardware module. Consideringembodiments in which hardware modules are temporarily configured (e.g.,programmed), each of the hardware modules need not be configured orinstantiated at any one instance in time. For example, where thehardware modules comprise a general-purpose processor configured usingsoftware, the general-purpose processor may be configured as respectivedifferent hardware modules at different times. Software may accordinglyconfigure a processor, for example, to constitute a particular hardwaremodule at one instance of time and to constitute a different hardwaremodule at a different instance of time.

Hardware modules can provide information to, and receive informationfrom, other hardware modules. Accordingly, the described hardwaremodules may be regarded as being communicatively coupled. Where multipleof such hardware modules exist contemporaneously, communications may beachieved through signal transmission (e.g., over appropriate circuitsand buses) that connect the hardware modules. In embodiments in whichmultiple hardware modules are configured or instantiated at differenttimes, communications between such hardware modules may be achieved, forexample, through the storage and retrieval of information in memorystructures to which the multiple hardware modules have access. Forexample, one hardware module may perform an operation and store theoutput of that operation in a memory device to which it iscommunicatively coupled. A further hardware module may then, at a latertime, access the memory device to retrieve and process the storedoutput. Hardware modules may also initiate communications with input oroutput devices, and can operate on a resource (e.g., a collection ofinformation).

The various operations of example methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors may constitute processor-implemented modulesthat operate to perform one or more operations or functions. The modulesreferred to herein may, in some example embodiments, compriseprocessor-implemented modules.

Similarly, the methods described herein may be at least partiallyprocessor-implemented. For example, at least some of the operations of amethod may be performed by one or processors or processor-implementedhardware modules. The performance of certain of the operations may bedistributed among the one or more processors, not only residing within asingle machine, but deployed across a number of machines. In someexample embodiments, the processor or processors may be located in asingle location (e.g., within a home environment, an office environmentor as a server farm), while in other embodiments the processors may bedistributed across a number of locations.

The one or more processors may also operate to support performance ofthe relevant operations in a “cloud computing” environment or as a“software as a service” (SaaS). For example, at least some of theoperations may be performed by a group of computers (as examples ofmachines including processors), these operations being accessible via anetwork (e.g., the Internet) and via one or more appropriate interfaces(e.g., application program interfaces (APIs).)

The performance of certain of the operations may be distributed amongthe one or more processors, not only residing within a single machine,but deployed across a number of machines. In some example embodiments,the one or more processors or processor-implemented modules may belocated in a single geographic location (e.g., within a homeenvironment, an office environment, or a server farm). In other exampleembodiments, the one or more processors or processor-implemented modulesmay be distributed across a number of geographic locations.

Some portions of this specification are presented in terms of algorithmsor symbolic representations of operations on data stored as bits orbinary digital signals within a machine memory (e.g., a computermemory). These algorithms or symbolic representations are examples oftechniques used by those of ordinary skill in the data processing artsto convey the substance of their work to others skilled in the art. Asused herein, an “algorithm” is a self-consistent sequence of operationsor similar processing leading to a desired result. In this context,algorithms and operations involve physical manipulation of physicalquantities. Typically, but not necessarily, such quantities may take theform of electrical, magnetic, or optical signals capable of beingstored, accessed, transferred, combined, compared, or otherwisemanipulated by a machine. It is convenient at times, principally forreasons of common usage, to refer to such signals using words such as“data,” “content,” “bits,” “values,” “elements,” “symbols,”“characters,” “terms,” “numbers,” “numerals,” or the like. These words,however, are merely convenient labels and are to be associated withappropriate physical quantities.

Unless specifically stated otherwise, discussions herein using wordssuch as “processing,” “computing,” “calculating,” “determining,”“presenting,” “displaying,” or the like may refer to actions orprocesses of a machine (e.g., a computer) that manipulates or transformsdata represented as physical (e.g., electronic, magnetic, or optical)quantities within one or more memories (e.g., volatile memory,non-volatile memory, or a combination thereof), registers, or othermachine components that receive, store, transmit, or displayinformation.

As used herein any reference to “one embodiment” or “an embodiment”means that a particular element, feature, structure, or characteristicdescribed in connection with the embodiment is included in at least oneembodiment. The appearances of the phrase “in one embodiment” in variousplaces in the specification are not necessarily all referring to thesame embodiment.

Some embodiments may be described using the expression “coupled” and“connected” along with their derivatives. It should be understood thatthese terms are not intended as synonyms for each other. For example,some embodiments may be described using the term “connected” to indicatethat two or more elements are in direct physical or electrical contactwith each other. In another example, some embodiments may be describedusing the term “coupled” to indicate that two or more elements are indirect physical or electrical contact. The term “coupled,” however, mayalso mean that two or more elements are not in direct contact with eachother, but yet still co-operate or interact with each other. Theembodiments are not limited in this context.

As used herein, the terms “comprises,” “comprising,” “includes,”“including,” “has,” “having” or any other variation thereof, areintended to cover a non-exclusive inclusion. For example, a process,method, article, or apparatus that comprises a list of elements is notnecessarily limited to only those elements but may include otherelements not expressly listed or inherent to such process, method,article, or apparatus. Further, unless expressly stated to the contrary,“or” refers to an inclusive or and not to an exclusive or. For example,a condition A or B is satisfied by any one of the following: A is true(or present) and B is false (or not present), A is false (or notpresent) and B is true (or present), and both A and B are true (orpresent).

In addition, use of the “a” or “an” are employed to describe elementsand components of the embodiments herein. This is done merely forconvenience and to give a general sense of the invention. Thisdescription should be read to include one or at least one and thesingular also includes the plural unless it is obvious that it is meantotherwise.

Upon reading this disclosure, those of skill in the art will appreciatestill additional alternative structural and functional designs throughthe disclosed principles herein. Thus, while particular embodiments andapplications have been illustrated and described, it is to be understoodthat the disclosed embodiments are not limited to the preciseconstruction and components disclosed herein. Various modifications,changes and variations, which will be apparent to those skilled in theart, may be made in the arrangement, operation and details of the methodand apparatus disclosed herein without departing from the spirit andscope defined in the appended claims.

What is claimed is:
 1. A method comprising: receiving a set of images,wherein each image has a first bit depth and corresponds to a slice of athree-dimensional object; combining a first subset of the set of imagesinto a first combined image having a second bit depth lower than thefirst bit depth, wherein combining the first subset of the set of imagescomprises: for each pixel of each image in the first subset of the setof images: determining a location in the first combined image associatedwith the pixel using a pixel arrangement configured to increase anefficiency of a lossless image compression algorithm; determining a newpixel having the second bit depth different from the pixel having thefirst bit depth; and storing the new pixel in the first combined imageat the location in the first combined image associated with the pixel;combining a second subset of the set of images into a second combinedimage, the second combined image comprising pixels having the second bitdepth; compressing the first combined image and the second combinedimage using the lossless image compression algorithm; and transmittingthe compressed first combined image and the second compressed secondcombined image to a client device.
 2. The method of claim 1, whereincombining the first subset of the set of images into the first combinedimage comprises: interlacing images in the first subset of the set ofimages.
 3. The method of claim 1, further comprising: combining imagesin the first subset of the set of images into a third combined image,wherein the first combined image and the third combined image have thesecond bit depth.
 4. The method of claim 1, wherein the new pixel havingthe second bit depth comprises a subset of bits of the pixel having thefirst bit depth.
 5. The method of claim 1, wherein the set of images aremedical images captured using a medical imaging device.
 6. The method ofclaim 5, wherein the medical imaging device is one of a computertomography (CT) scanner, a magnetic resonance imaging (MRI) scanner, anX-ray machine, and an ultrasound machine.
 7. The method of claim 1,wherein the set of images are received using a Digital Imaging andCommunication in Medicine (DICOM) standard.
 8. The method of claim 1,wherein a slice is a two-dimensional image of the three-dimensionalobject across a specific plane at a specific depth.
 9. The method ofclaim 1, wherein the first subset of the set of images includes imagescorresponding to a first portion of the three-dimensional object, andwherein the second subset of the set of images includes imagescorresponding to a second portion of the three-dimensional object.
 10. Amethod comprising: receiving one or more compressed combined images,each compressed combined image generated by combining a plurality ofimages, each image corresponding to a slice of a three-dimensionalobject and having a first bit depth, wherein each compressed combinedimage is generated from at least of the plurality of images combined by:determining a location in the first combined image associated with eachpixel of the plurality of images using a pixel arrangement configured toincrease an efficiency of a lossless image compression algorithm;determining a new pixel having a second bit depth different from thepixel having the first bit depth; and storing the new pixel in the firstcombined image at the location in the first combined image associatedwith the pixel; decompressing the received one or more compressedcombined images to generate one or more decompressed combined images;for each of the one or more decompressed combined images: extracting,from the decompressed combined image, the plurality of images;generating, from the extracted plurality of images, a texture map for athree-dimensional model; and rendering the three-dimensional model usingthe generated texture map.
 11. The method of claim 10, whereinextracting the plurality of images comprises: extracting a first subsetof images from a first decompressed combined image; and extracting asecond subset of images from a second decompressed combined image. 12.The method of claim 10, wherein extracting the plurality of imagescomprises: deinterlacing the one or more decompressed combined images.13. The method of claim 10, wherein each decompressed combined image ofthe one or more decompressed combined images has the second bit depth.14. The method of claim 10, wherein extracting the plurality of imagescomprises: extracting a first image of the plurality of images, theextraction comprising: for each pixel of the first image, determining afirst subset of bits from a pixel of a first decompressed combinedimage, and determining a second subset of bits from a pixel of a seconddecompressed combined image.
 15. The method of claim 10, furthercomprising: generating, from the extracted plurality of images, athree-dimensional mesh for the three-dimensional model.
 16. The methodof claim 15, wherein generating the three-dimensional mesh comprises:determining a set of vertices from each image of the extracted pluralityof images; and connecting subsets of the set of vertices to form thethree-dimensional mesh.
 17. The method of claim 10, wherein theplurality of images are medical images captured using a medical imagingdevice.
 18. The method of claim 17, wherein the medical imaging deviceis one of a computer tomography (CT) scanner, a magnetic resonanceimaging (MRI) scanner, an X-ray machine, and an ultrasound machine. 19.The method of claim 10, wherein a slice is a two-dimensional image ofthe three-dimensional object across a specific plane at a specificdepth.